Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

• Define a Function that Makes a Graph
• Question 1: Use yfinance to Extract Stock Data
• Question 2: Use Webscraping to Extract Tesla Revenue Data
• Question 3: Use yfinance to Extract Stock Data
• Question 4: Use Webscraping to Extract GME Revenue Data
• Question 5: Plot Tesla Stock Graph
• Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

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import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup as BS
import plotly.graph_objects as go
from plotly.subplots import make_subplots

Define Graphing Function

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows = 2, cols = 1, shared_xaxes = True,
                        subplot_titles = ("Historical Share Price", "Historical Revenue"),
                        vertical_spacing = .3)
    fig.add_trace(go.Scatter(x = pd.to_datetime(stock_data.Date, infer_datetime_format = True),
                             y = stock_data.Close.astype("float"), name = "Share Price"), row = 1, col = 1)
    fig.add_trace(go.Scatter(x = pd.to_datetime(revenue_data.Date, infer_datetime_format = True),
                             y = revenue_data.Revenue.astype("float"), name = "Revenue"), row = 2, col = 1)
    fig.update_xaxes(title_text = "Date", row = 1, col = 1)
    fig.update_xaxes(title_text = "Date", row = 2, col = 1)
    fig.update_yaxes(title_text = "Price ($US)", row = 1, col = 1)
    fig.update_yaxes(title_text = "Revenue ($US Millions)", row = 2, col = 1)
    fig.update_layout(showlegend = False,
                      height = 900,
                      title = stock,
                      xaxis_rangeslider_visible = True)
    fig.show()

Question 1: Use yfinance to Extract Stock Data

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

tesla = yf.Ticker("TSLA")

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.

tesla_data = tesla.history(period="max")

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

tesla_data.reset_index(inplace = True)
tesla_data.head()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2010-06-29	3.800	5.000	3.508	4.778	93831500	0	0.0
1	2010-06-30	5.158	6.084	4.660	4.766	85935500	0	0.0
2	2010-07-01	5.000	5.184	4.054	4.392	41094000	0	0.0
3	2010-07-02	4.600	4.620	3.742	3.840	25699000	0	0.0
4	2010-07-06	4.000	4.000	3.166	3.222	34334500	0	0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data

Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue. Save the text of the response as a variable named html_data.

tesla_page = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
tesla_html = requests.get(tesla_page).text

Parse the html data using beautiful_soup.

tesla_soup = BS(tesla_html, "html.parser")

Using beautiful soup extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

tesla_revenue = pd.DataFrame(columns =["Date",
                                       "Revenue"])

for row in tesla_soup.find("tbody").find_all("tr"):
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text.replace("$", "").replace(",", "")

    tesla_revenue = tesla_revenue.append({"Date": date,
                                          "Revenue": revenue},
                                          ignore_index = True)

Click here if you need help removing the dollar sign and comma

``` If you parsed the HTML table by row and column you can use the replace function on the string revenue = col[1].text.replace("$", "").replace(",", "") If you use the read_html function you can use the replace function on the string representation of the column tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace("$", "").str.replace(",", "") ```

Remove the rows in the dataframe that are empty strings or are NaN in the Revenue column. Print the entire tesla_revenue DataFrame to see if you have any.

tesla_revenue

	Date	Revenue
0	2020	31536
1	2019	24578
2	2018	21461
3	2017	11759
4	2016	7000
5	2015	4046
6	2014	3198
7	2013	2013
8	2012	413
9	2011	204
10	2010	117
11	2009	112
12	2008	15

Click here if you need help removing the Nan or empty strings

``` If you have NaN in the Revenue column tesla_revenue.dropna(inplace=True) If you have emtpty string in the Revenue column tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""] ```

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

tesla_revenue.tail()

	Date	Revenue
8	2012	413
9	2011	204
10	2010	117
11	2009	112
12	2008	15

Question 3: Use yfinance to Extract Stock Data

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

game = yf.Ticker("GME")

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.

gme_data = game.history(period="max")

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

gme_data.reset_index(inplace = True)
gme_data.head()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2002-02-13	6.480513	6.773399	6.413183	6.766666	19054000	0.0	0.0
1	2002-02-14	6.850831	6.864296	6.682506	6.733003	2755400	0.0	0.0
2	2002-02-15	6.733001	6.749833	6.632006	6.699336	2097400	0.0	0.0
3	2002-02-19	6.665671	6.665671	6.312189	6.430017	1852600	0.0	0.0
4	2002-02-20	6.463681	6.648838	6.413183	6.648838	1723200	0.0	0.0

Question 4: Use Webscraping to Extract GME Revenue Data

Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue. Save the text of the response as a variable named html_data.

gme_page = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
gme_html = requests.get(gme_page).text

Parse the html data using beautiful_soup.

gme_soup = BS(gme_html, "html.parser")

Using beautiful soup extract the table with GameStop Quarterly Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.

gme_revenue = pd.DataFrame(columns =["Date",
                                     "Revenue"])

for row in gme_soup.find("tbody").find_all("tr"):
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text.replace("$", "").replace(",", "")

    gme_revenue = gme_revenue.append({"Date": date,
                                      "Revenue": revenue},
                                     ignore_index = True)

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

gme_revenue.tail()

	Date	Revenue
11	2009	8806
12	2008	7094
13	2007	5319
14	2006	3092
15	2005	1843

Question 5: Plot Tesla Stock Graph

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla')

make_graph(tesla_data, tesla_revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop').

make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log

Date (YYYY-MM-DD)	Version	Changed By	Change Description
2020-11-10	1.1	Malika Singla	Deleted the Optional part
2020-08-27	1.0	Malika Singla	Added lab to GitLab

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